Myocardial instability such as atrial and/or ventricular fibrillation, tachycardia, bradycardia, myocardial infarction, and the like, may pose significant health risks to patients. For example, pulmonary edema may lead to respiratory failure of patients. Ventricular arrhythmia such as ventricular fibrillation may lead to cardiac arrest and death. One or more physiological parameters of the patients related to cardiac function may be tracked in order to treat the patient and thus reduce the likelihood of sudden cardiac arrest and death.
For example, congestive heart failure (CHF) is an imbalance in pump function in which the heart fails to maintain appropriate blood circulation. The most severe manifestation of CHF, cardiogenic pulmonary edema (PE), develops when this imbalance causes a patient's heart to have difficulty clearing or moving fluid through or from the left ventricle out of the heart. The fluid may back up through the patient's circulatory system and accumulate in the patient's lungs. The amount of fluid that is accumulated in the lung may be one indicator of cardiogenic pulmonary edema. Changes in electrical impedance across the heart and lung may be tracked in order to determine the severity of the myocardial instability, as well as to track the onset and termination of events or episodes of heart failure. These changes in electrical impedance represent one physiological parameter of a variety of physiological parameters that may be used to track episodes of myocardial instability. Conventional systems exist that monitor sets of physiologic parameters and identify myocardial instability when the physiologic parameters change by predetermined amounts.
However, conventional systems have experienced disadvantages when monitoring physiological parameters for the purpose of tracking the occurrence and frequency of episodes of myocardial instability. For example, in known systems where too few parameters may be monitored, a change in one parameter may incorrectly be associated with an episode of myocardial instability when in fact the parameter change is due to something else. Moreover, the parameters that are monitored may be dependent on one another. For example, an increase in the measured value of a first parameter may result in a related increase in the measured value of a second parameter. When both of the first and second parameters are tracked to determine the onset of an episode of myocardial instability, then an increase in the first parameter, that is unrelated to myocardial instability, may result in a corresponding increase in the second parameter to be attributed to myocardial instability. As a result, when a relatively small number of parameters are monitored and/or the monitored parameters are dependent on one another, the number of falsely detected episodes of myocardial instability, or “false positives,” may be undesirably high.
Thus, a need exists to improve the accuracy of methods and systems that track the occurrence of events of myocardial instability and to decrease the number of false positive as detected events of myocardial instability.